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可解释的深度学习揭示了 E 盒基序在抑制人类免疫球蛋白可变区中 AGCT 基序的体细胞高频突变中的作用。

Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

机构信息

Department of Biochemistry and Cell Biology, Stony Brook University, Stony Brook, NY, United States.

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, United States.

出版信息

Front Immunol. 2024 May 28;15:1407470. doi: 10.3389/fimmu.2024.1407470. eCollection 2024.


DOI:10.3389/fimmu.2024.1407470
PMID:38863710
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11165027/
Abstract

INTRODUCTION: Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially within WRCH motifs (where W=A or T, R=A or G and H=A, C or T). However, it has been consistently observed that the mutability of WRCH motifs varies substantially, with large variations in mutation frequency even between multiple occurrences of the same motif within a single V region. This has led to the notion that the immediate sequence context of WRCH motifs contributes to mutability. Recent studies have highlighted the potential role of local DNA sequence features in promoting mutagenesis of AGCT, a commonly mutated WRCH motif. Intriguingly, AGCT motifs closer to 5' ends of V regions, within the framework 1 (FW1) sub-region1, mutate less frequently, suggesting an SHM-suppressing sequence context. METHODS: Here, we systematically examined the basis of AGCT positional biases in human SHM datasets with DeepSHM, a machine-learning model designed to predict SHM patterns. This was combined with integrated gradients, an interpretability method, to interrogate the basis of DeepSHM predictions. RESULTS: DeepSHM predicted the observed positional differences in mutation frequencies at AGCT motifs with high accuracy. For the conserved, lowly mutating AGCT motifs in FW1, integrated gradients predicted a large negative contribution of 5'C and 3'G flanking residues, suggesting that a CAGCTG context in this location was suppressive for SHM. CAGCTG is the recognition motif for E-box transcription factors, including E2A, which has been implicated in SHM. Indeed, we found a strong, inverse relationship between E-box motif fidelity and mutation frequency. Moreover, E2A was found to associate with the V region locale in two human B cell lines. Finally, analysis of human SHM datasets revealed that naturally occurring mutations in the 3'G flanking residues, which effectively ablate the E-box motif, were associated with a significantly increased rate of AGCT mutation. DISCUSSION: Our results suggest an antagonistic relationship between mutation frequency and the binding of E-box factors like E2A at specific AGCT motif contexts and, therefore, highlight a new, suppressive mechanism regulating local SHM patterns in human V regions.

摘要

简介:通过激活诱导脱氨酶(AID)对免疫球蛋白可变(V)区进行体细胞超突变(SHM)是针对病原体和疫苗抗原产生强大、长期体液免疫的关键。AID 优先在 WRCH 基序(其中 W=A 或 T,R=A 或 G,H=A、C 或 T)内突变胞嘧啶。然而,人们一直观察到 WRCH 基序的突变率存在很大差异,即使在单个 V 区的同一基序的多个发生中,突变频率也有很大差异。这导致了这样一种观点,即 WRCH 基序的直接序列上下文有助于突变性。最近的研究强调了局部 DNA 序列特征在促进 AGCT 突变中的潜在作用,AGCT 是一个常见的突变 WRCH 基序。有趣的是,更靠近 V 区 5'端的 AGCT 基序,在框架 1(FW1)亚区 1 内,突变频率较低,表明存在 SHM 抑制序列上下文。

方法:在这里,我们使用深度学习 SHM(DeepSHM),一种旨在预测 SHM 模式的机器学习模型,系统地检查了人类 SHM 数据集中原位 AGCT 偏倚的基础。这与积分梯度(一种解释性方法)相结合,以探究 DeepSHM 预测的基础。

结果:DeepSHM 以高精度预测了 AGCT 基序中观察到的突变频率的位置差异。对于 FW1 中保守、突变率低的 AGCT 基序,积分梯度预测 5'C 和 3'G 侧翼残基的贡献很大,表明该位置的 CAGCTG 上下文对 SHM 具有抑制作用。CAGCTG 是 E 盒转录因子(包括 E2A)的识别基序,E2A 已被牵连到 SHM 中。事实上,我们发现 E 盒基序保真度与突变频率之间存在强烈的反比关系。此外,在两个人类 B 细胞系中发现 E2A 与 V 区位置相关。最后,对人类 SHM 数据集的分析表明,3'G 侧翼残基的自然突变有效地消除了 E 盒基序,与 AGCT 突变率的显著增加有关。

讨论:我们的结果表明,在特定的 AGCT 基序背景下,突变频率与 E 盒因子(如 E2A)的结合之间存在拮抗关系,因此突出了一种新的、抑制性机制,调节了人类 V 区的局部 SHM 模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/f7af756842d6/fimmu-15-1407470-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/3b62c2dc1ba4/fimmu-15-1407470-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/6a8340c1c790/fimmu-15-1407470-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/210e175d1a5b/fimmu-15-1407470-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/6befc01a3536/fimmu-15-1407470-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/b7b1b2ff3913/fimmu-15-1407470-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/405815a0179e/fimmu-15-1407470-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/f07926b16019/fimmu-15-1407470-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/f7af756842d6/fimmu-15-1407470-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/3b62c2dc1ba4/fimmu-15-1407470-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/6a8340c1c790/fimmu-15-1407470-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/210e175d1a5b/fimmu-15-1407470-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/6befc01a3536/fimmu-15-1407470-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/b7b1b2ff3913/fimmu-15-1407470-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/405815a0179e/fimmu-15-1407470-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/f07926b16019/fimmu-15-1407470-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/91fc/11165027/f7af756842d6/fimmu-15-1407470-g008.jpg

相似文献

[1]
Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions.

Front Immunol. 2024

[2]
Characterization of DNA G-Quadruplex Structures in Human Immunoglobulin Heavy Variable (IGHV) Genes.

Front Immunol. 2021

[3]
A critical context-dependent role for E boxes in the targeting of somatic hypermutation.

J Immunol. 2013-7-8

[4]
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Mol Immunol. 2008-11

[5]
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J Immunol. 2007-4-1

[6]
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Immunology. 2010-11-11

[7]
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iScience. 2021-12-20

[8]
Recombinase-mediated cassette exchange as a novel method to study somatic hypermutation in Ramos cells.

mBio. 2011-10-11

[9]
Expression of human AID in yeast induces mutations in context similar to the context of somatic hypermutation at G-C pairs in immunoglobulin genes.

BMC Immunol. 2005-6-10

[10]
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Leuk Lymphoma. 2016

本文引用的文献

[1]
Mesoscale DNA feature in antibody-coding sequence facilitates somatic hypermutation.

Cell. 2023-5-11

[2]
The transcription factor E2A can bind to and cleave single-stranded immunoglobulin heavy chain locus DNA.

Mol Immunol. 2023-1

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Profiling the peripheral blood T cell receptor repertoires of gastric cancer patients.

Front Immunol. 2022

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Annu Rev Immunol. 2022-4-26

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Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting.

iScience. 2021-12-20

[6]
Ig Enhancers Increase RNA Polymerase II Stalling at Somatic Hypermutation Target Sequences.

J Immunol. 2022-1-1

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Nucleic Acids Res. 2022-1-7

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Role of Dot1L and H3K79 methylation in regulating somatic hypermutation of immunoglobulin genes.

Proc Natl Acad Sci U S A. 2021-7-20

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Twelve years of SAMtools and BCFtools.

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Position-Dependent Differential Targeting of Somatic Hypermutation.

J Immunol. 2020-12-15

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